Designing optimal sampling schemes

Designing optimal sampling schemes. Swärd, J., Elvander, F., & Jakobsson, A. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 912-916, Aug, 2017.

Paper doi abstract bibtex

In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the sampling scheme to take such uncertainty into account. Numerical examples illustrate the efficiency of the proposed scheme.

@InProceedings{8081340,
  author = {J. Swärd and F. Elvander and A. Jakobsson},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Designing optimal sampling schemes},
  year = {2017},
  pages = {912-916},
  abstract = {In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the sampling scheme to take such uncertainty into account. Numerical examples illustrate the efficiency of the proposed scheme.},
  keywords = {convex programming;signal sampling;signal measurements;nonlinear functions;convex optimization problem;sensor selection problem;optimal sampling schemes;Nuclear magnetic resonance;Optimization;Upper bound;Signal processing;Convex functions;Estimation;Uncertainty},
  doi = {10.23919/EUSIPCO.2017.8081340},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570345475.pdf},
}

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